Model predictive BESS control for demand charge management and PV-utilization improvement

Abstract

Adoption of battery energy storage systems for behind-the-meters application offers valuable benefits for demand charge management as well as increasing PV-utilization.The key point is that while the benefit/cost ratio for a single application may not be favorable for economic benefits of storage systems, stacked services can provide multiple revenue streams for the same investment. Under this framework, we propose a model predictive controller to reduce demand charge cost and enhance PV-utilization level simultaneously. Different load patterns have been considered in this study and results are compared to the conventional rule-based controller. The results verified that the proposed controller provides satisfactory performance by improving the PV-utilization rate between 60% to 80% without significant changes in demand charge (DC) saving. Furthermore, our results suggest that batteries can be used for stacking multiple services to improve their benefits. Quantitative analysis for PV-utilization as a function of battery size and prediction time window has also been carried out.Index Terms-Battery energy storage, photovoltaic power generation, behind-the-meter, demand charge, PV-utilization. NOMENCLATUREη pv PV-utilization rate; P pur g , P sell g purchased/sold power from/to grid, kW; P load , P pv Load/PV power, kW; P cha b , P dis b BESS charge/discharge power, kW; P max b BESS charge/discharge power limit, kW; SOC BESS state-of-charge; SOC min ,SOC max BESS state-of-charge limits; E sell BESS , E sell N oBESS Excess energy sold to the grid with or without BESS, kWh; DCT Demand charge threshold, kW; λ DC Demand charge rate, $/kW C tp Battery throughput cost, $/kW

A Two-Level Model Predictive Control-Based Approach for Building Energy Management including Photovoltaics, Energy Storage, Solar Forecasting and Building Loads

“…MPC was introduced for chemical process control in the late 1970s [19]. Recently, it has become a popular choice for many applications, including power systems; e.g., (1) as a ramp rate controller in wind turbines [20,21], (2) for controlling battery energy storage (BES) systems in conjunction with photovoltaic (PV) for smoothing solar injections [22] with a demonstrated reduction in cost and improvements in overall system performance without addressing ramp rate and PV forecasting errors in the system, (3) a MPC-based controller for reducing demand charges while increasing PV utilization without consideration of PV ramp rate constraints [23].…”

Micro-Tracked CPV Can Be Cost Competitive With PV in Behind-The-Meter Applications With Demand Charges

“…Microgrid optimization occurs at three levels (i) control of voltage, current and power, (ii) control of power quality, and (iii) control of scheduling and economics, and a comprehensive review is provided by Meng et al (2016). Focusing here on optimal scheduling and economics, various approaches have been used including: minimizing annual net cost of operation (Azzopardi and Mutale, 2009), minimizing cost of electricity consumed in a commercial building (Marnay et al, 2008;Fina et al, 2017;Mariaud et al, 2017), optimal participation in an energy market (Celli et al, 2005), optimal integration of distributed wind generation into the grid (Zhou and Francois, 2011), optimal management of a rural microgrid (Zhang et al, 2012), optimal PV-battery system for demand charge or peak reduction (Hanna et al, 2014;Khalilpour and Vassallo, 2016;Park and Lappas, 2017;Parra et al, 2017;McLaren et al, 2018;Raoufat et al, 2018), and optimal PV-storage capacity in terms of grid impact and PV utilization rate (Brusco et al, 2016;Merei et al, 2016;Cervantes and Choobineh, 2018;Freitas et al, 2018;Raoufat et al, 2018). These papers focus on PV microgrids.…”

“…Linear programming has often been used to determine optimal PV-storage system sizes (Hanna et al, 2014;Khalilpour and Vassallo, 2016;and Mariaud et al, 2017;Cervantes and Choobineh, 2018), however Zhang et al (2017a) use three rule-based operation strategies which optimized the system using non-linear programming. Under current market prices, battery storage was found in some cases to be uneconomical and delivering minimal cost reduction (Merei et al, 2016;Mariaud et al, 2017;Zhang et al, 2017a;Raoufat et al, 2018). In such circumstances, it is possible for batteries to provide additional services such as ramp rate control and frequency regulation (Raoufat et al, 2018).…”

“…Under current market prices, battery storage was found in some cases to be uneconomical and delivering minimal cost reduction (Merei et al, 2016;Mariaud et al, 2017;Zhang et al, 2017a;Raoufat et al, 2018). In such circumstances, it is possible for batteries to provide additional services such as ramp rate control and frequency regulation (Raoufat et al, 2018). Conversely, storage integration yielded financially beneficial results at lower technology cost limits as modeled in McLaren et al (2018), nearly doubled net present value of cash flow in Khalilpour and Vassallo (2016), decreased maximum load by nearly 10% in Hanna et al (2014), supplied energy at 34% less cost than network prices in Park and Lappas (2017), and produced electricity bill savings of up to 12% in Brusco et al (2016).…”

Model Predictive Control of Vehicle Charging Stations in Grid-Connected Microgrids: An Implementation Study

“…Solving the optimal control problem in systems that can provide short term flexibility to the power grid and photovoltaic (PV) power sources can be done by optimizing for grid fluctuations [6], operational cost [7], or self-consumption [8]. In such an optimal control problem, if battery energy storage systems (BESSs) are utilized, BESSs are charged when there is surplus PV power and discharged when there is a lack of PV power.…”